The document introduces semantic web technologies and provides an example of how data from multiple sources can be integrated and queried using these technologies. It shows book data from two sources being exported as RDF triples and merged by identifying common resources. Additional information is then added from Wikipedia to further enrich the merged data and enable more complex queries across the integrated sources. The key concepts of RDF, URIs, blank nodes, and ontologies are explained as the basis for representing and linking data in this way on the semantic web.
For everybody who gets tired of questions like “when is the Semantic Web actually going to happen”, or any other suggestion that the Semantic Web programme is “only vision, no progress”.
For everybody who gets tired of questions like “when is the Semantic Web actually going to happen”, or any other suggestion that the Semantic Web programme is “only vision, no progress”.
A non-technical introduction to Linked Data, from a Cultural Heritage organization's perspective. This presentation is from the Provenance Index workshop at the Getty in 2016, with an emphasis on why Linked Data is valuable, as well as how it works in general. [Please see speaker notes for explanations of image slides]
Lecture at the advanced course on Data Science of the SIKS research school, May 20, 2016, Vught, The Netherlands.
Contents
-Why do we create Linked Open Data? Example questions from the Humanities and Social Sciences
-Introduction into Linked Open Data
-Lessons learned about the creation of Linked Open Data (link discovery, knowledge representation, evaluation).
-Accessing Linked Open Data
Various FAIR criteria pertaining to machine interaction with scholarly artifacts can commonly be addressed by means of repository-wide affordances that are uniformly provided for all hosted artifacts rather than through artifact-specific interventions. If various repository platforms provide such affordances in an interoperable manner, devising tools - for both human and machine use - that leverage them becomes easier.
My involvement, over the years, in a range of interoperability efforts has brought the insight that two factors strongly influence adoption: addressing a burning issue and delivering a KISS solution to tackle it. Undoubtedly, FAIR and FAIR DOs are burning issues. FAIR Signposting <https://signposting.org/FAIR/> is an ad-hoc repository interoperability effort that squarely fits in this problem space and that purposely specifies a KISS solution, hoping to inspire wide adoption.
Interlinking Standardized OpenStreetMap Data and Citizen Science Data in the ...Werner Leyh
Abstract. The aim of this work is to explore the opportunities offered by
semantic standardization to interlink primary “spatial data” (GI) from “Open-
StreetMap” (OSM) with repositories of the “Linked Open Data Cloud” (LOD).
Research in natural sciences can generate vast amounts of spatial data, where
Wikidata could be considered as the central hub between more detailed natural
science hubs on the spatial semantic web. Wikidata is a world readable and
writable community-driven knowledge base. It offers the opportunity to collaboratively
construct an open access knowledge graph that spans biology,
medicine, and all other domains of knowledge. In this study, we discuss
the opportunities and challenges provided by exploring Wikidata as a central
integration facility by interlink it with OSM, a popular, community driven
collection of free geographic data. This is empowered by the reuse of terms
and properties from commonly understood controlled vocabularies that
represent their respective well-identified knowledge domains.
URL: https://www.springerprofessional.de/en/interlinking-standardized-openstreetmap-data-and-citizen-science/13302088
DOI: https://doi.org/10.1007/978-3-319-60366-7_9
Werner Leyh, Homero Fonseca Filho
University of São Paulo (USP), São Paulo, Brazil
WernerLeyh@yahoo.com
Guest lecture at the Syracuse University School of Information Studies eScience Librarianship Lecture Series (08 Dec 2011).
Description: It’s your government, is it your data? New approaches to building interlinked catalogs of government-produced data. Dr. John S. Erickson, Director of Web Science Operations for the Tetherless World Constellation at Rensselaer Polytechnic Institute will present technical methods being developed to manage the delivery of large-scale open government data projects based on semantic web and linked data best practices.
A non-technical introduction to Linked Data, from a Cultural Heritage organization's perspective. This presentation is from the Provenance Index workshop at the Getty in 2016, with an emphasis on why Linked Data is valuable, as well as how it works in general. [Please see speaker notes for explanations of image slides]
Lecture at the advanced course on Data Science of the SIKS research school, May 20, 2016, Vught, The Netherlands.
Contents
-Why do we create Linked Open Data? Example questions from the Humanities and Social Sciences
-Introduction into Linked Open Data
-Lessons learned about the creation of Linked Open Data (link discovery, knowledge representation, evaluation).
-Accessing Linked Open Data
Various FAIR criteria pertaining to machine interaction with scholarly artifacts can commonly be addressed by means of repository-wide affordances that are uniformly provided for all hosted artifacts rather than through artifact-specific interventions. If various repository platforms provide such affordances in an interoperable manner, devising tools - for both human and machine use - that leverage them becomes easier.
My involvement, over the years, in a range of interoperability efforts has brought the insight that two factors strongly influence adoption: addressing a burning issue and delivering a KISS solution to tackle it. Undoubtedly, FAIR and FAIR DOs are burning issues. FAIR Signposting <https://signposting.org/FAIR/> is an ad-hoc repository interoperability effort that squarely fits in this problem space and that purposely specifies a KISS solution, hoping to inspire wide adoption.
Interlinking Standardized OpenStreetMap Data and Citizen Science Data in the ...Werner Leyh
Abstract. The aim of this work is to explore the opportunities offered by
semantic standardization to interlink primary “spatial data” (GI) from “Open-
StreetMap” (OSM) with repositories of the “Linked Open Data Cloud” (LOD).
Research in natural sciences can generate vast amounts of spatial data, where
Wikidata could be considered as the central hub between more detailed natural
science hubs on the spatial semantic web. Wikidata is a world readable and
writable community-driven knowledge base. It offers the opportunity to collaboratively
construct an open access knowledge graph that spans biology,
medicine, and all other domains of knowledge. In this study, we discuss
the opportunities and challenges provided by exploring Wikidata as a central
integration facility by interlink it with OSM, a popular, community driven
collection of free geographic data. This is empowered by the reuse of terms
and properties from commonly understood controlled vocabularies that
represent their respective well-identified knowledge domains.
URL: https://www.springerprofessional.de/en/interlinking-standardized-openstreetmap-data-and-citizen-science/13302088
DOI: https://doi.org/10.1007/978-3-319-60366-7_9
Werner Leyh, Homero Fonseca Filho
University of São Paulo (USP), São Paulo, Brazil
WernerLeyh@yahoo.com
Guest lecture at the Syracuse University School of Information Studies eScience Librarianship Lecture Series (08 Dec 2011).
Description: It’s your government, is it your data? New approaches to building interlinked catalogs of government-produced data. Dr. John S. Erickson, Director of Web Science Operations for the Tetherless World Constellation at Rensselaer Polytechnic Institute will present technical methods being developed to manage the delivery of large-scale open government data projects based on semantic web and linked data best practices.
Se describe de forma breve y sencilla en que consiste el comportamiento politico colectivo e individual. Se analizsa los descrito en el libro de Ava Anduiza.
Ce parking fait partie du réseau de la fondation des Parkings, qui avec ses 40 ans, garde pour priorité le développement des parkings P+R et habitants. Parking utilisé pour accéder au quartier des Banques, aux instituts universitaires, aux galeries d’art et musées ainsi qu’aux nombreux commerces et restaurants.
La Infografía como Recurso Educativo para trabajar las Competencias en el aul...Rocío González
La infografía es un recurso que con un uso y propuesta adecuados puede convertirse en una herramienta de aprendizaje que nos permita trabajar por competencias en el aula, en especial en el aula de matemáticas.
Orientarte al Cliente es mas de lo que imaginaba cuando comenzamos con este proyecto, puedes medir el ROI de la Calidad en el Servicio, puedes medir la satisfaccion con instrumentos efectivos y no encuestas que nadie llena...
Módulo Control - Documento de la Lic. Graciela Chueque y el Prof. José Ravasi en el marco del 3º Congreso Provincial de Educación desarrollado los días 18, 19 y 20 de Julio de 2007 en la ciudad de Trelew, Chubut bajo la temática "Calidad Educativa: Un Proceso de Construcción Conjunta."
s developing mash-ups with Web 2.0 really much easier than using Semantic Web technologies? For instance, given a music style as an input, what it takes to retrieve data from online music archives (MusicBrainz, MusicBrainz D2R Server, MusicMoz) and event databases (EVDB)? What to merge them and to let the users explore the results? Are Semantic Web technologies up to this Web 2.0 challenge? This half-day tutorial shows how to realize a Semantic Web Application we named Music Event Explorer or shortly meex (try it!).
This tutorial explains the Data Web vision, some preliminary standards and technologies as well as some tools and technological building blocks developed by AKSW research group from Universität Leipzig.
The Semantic Web is about to grow up. By efforts such as the Linked Open Data initiative, we finally find ourselves at the edge of a Web of Data becoming reality. Standards such as OWL 2, RIF and SPARQL 1.1 shall allow us to reason with and ask complex structured queries on this data, but still they do not play together smoothly and robustly enough to cope with huge amounts of noisy Web data. In this talk, we discuss open challenges relating to querying and reasoning with Web data and raise the question: can the emerging Web of Data ever catch up with the now ubiquitous HTML Web?
Similar to Introduction to Semantic Web Technologies (20)
The convergence of Publishing and the WebIvan Herman
Presentation given at the Markup Forum in Stuttgart, in November 2015, on the convergence between digital publishing and Web technologies. A vision of a world where (Digital) Publishing is not as disjoint from the Web as it is today, when the separation between publishing "online", as web sites, and "offline" and/or packaged is diminished to zero.
Tutorial on RDFa, to be held at ISWC2010 in Shanghai, China. (I was supposed to hold the tutorial but last minute issues made it impossible for me to travel there...)
Presentation given during a tour of Australia, in May 2009. The targeted audience are people who are already familiar with the fundamentals of Semantic Web, and this presentation gives an overview of what is happening at W3C
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
4. How to build such a site 1. Site editors roam the Web for new facts may discover further links while roaming They update the site manually And the site gets soon out-of-date
5. How to build such a site 2. Editors roam the Web for new data published on Web sites “Scrape” the sites with a program to extract the information ie, write some codeto incorporate the new data Easily get out of date again…
6. How to build such a site 3. Editors roam the Web for new data via API-s Understand those… input, output arguments, datatypes used, etc Write some codeto incorporate the new data Easily get out of date again…
7. The choice of the BBC Use external, public datasets Wikipedia, MusicBrainz, … They are available as data not API-s or hidden on a Web site data can be extracted using, eg, HTTP requests or standard queries
8. In short… Use the Web of Data as a Content Management System Use the community at large as content editors
10. Data on the Web There are more an more data on the Web government data, health related data, general knowledge, company information, flight information, restaurants,… More and more applications rely on the availability of that data
11. But… data are often in isolation, “silos” Photo credit Alex (ajagendorf25), Flickr
12. Imagine… A “Web” where documents are available for download on the Internet but there would be no hyperlinks among them
14. Data on the Web is not enough… We need a proper infrastructure for a real Web of Data data is available on the Web accessible via standard Web technologies data are interlinked over the Web ie, data can be integrated over the Web This is where Semantic Web technologies come in
17. In what follows… We will use a simplistic example to introduce the main Semantic Web concepts
18. The rough structure of data integration Map the various data onto an abstract data representation make the data independent of its internal representation… Merge the resulting representations Start making queries on the whole! queries not possible on the individual data sets
21. 1st: export your data as a set of relations a:title The Glass Palace http://…isbn/000651409X a:year 2000 a:publisher a:city London a:author a:p_name Harper Collins a:name a:homepage http://www.amitavghosh.com Ghosh, Amitav
22. Some notes on the exporting the data Relations form a graph the nodes refer to the “real” data or contain some literal how the graph is represented in machine is immaterial for now
23. Some notes on the exporting the data Data export does not necessarily mean physical conversion of the data relations can be generated on-the-fly at query time via SQL “bridges” scraping HTML pages extracting data from Excel sheets etc. One can export part of the data
26. 2nd: export your second set of data http://…isbn/000651409X Le palais des miroirs f:original f:titre f:auteur http://…isbn/2020386682 f:traducteur f:nom f:nom Ghosh, Amitav Besse, Christianne
27. 3rd: start merging your data a:title The Glass Palace http://…isbn/000651409X a:year 2000 a:publisher a:city London a:author Harper Collins a:p_name http://…isbn/000651409X a:name a:homepage Le palais des miroirs f:original Ghosh, Amitav http://www.amitavghosh.com f:titre f:auteur http://…isbn/2020386682 f:traducteur f:nom f:nom Ghosh, Amitav Besse, Christianne
28. 3rd: start merging your data (cont) a:title The Glass Palace http://…isbn/000651409X a:year 2000 Same URI! a:publisher a:city London a:author Harper Collins a:p_name http://…isbn/000651409X a:name a:homepage Le palais des miroirs f:original Ghosh, Amitav http://www.amitavghosh.com f:titre f:auteur http://…isbn/2020386682 f:traducteur f:nom f:nom Ghosh, Amitav Besse, Christianne
29. 3rd: start merging your data a:title The Glass Palace http://…isbn/000651409X a:year 2000 a:publisher a:city London a:author Harper Collins a:p_name f:original f:auteur a:name a:homepage Le palais des miroirs Ghosh, Amitav http://www.amitavghosh.com f:titre http://…isbn/2020386682 f:traducteur f:nom f:nom Ghosh, Amitav Besse, Christianne
30. Start making queries… User of data “F” can now ask queries like: “give me the title of the original” well, … « donnes-moi le titre de l’original » This information is not in the dataset “F”… …but can be retrieved by merging with dataset “A”!
31. However, more can be achieved… We “feel” that a:author and f:auteur should be the same But an automatic merge doest not know that! Let us add some extra information to the merged data: a:author same as f:auteur both identify a “Person” a term that a community may have already defined: a “Person” is uniquely identified by his/her name and, say, homepage it can be used as a “category” for certain type of resources
32. 3rd revisited: use the extra knowledge a:title The Glass Palace http://…isbn/000651409X 2000 a:year Le palais des miroirs f:original f:titre a:publisher a:city London http://…isbn/2020386682 a:author Harper Collins a:p_name f:auteur f:traducteur r:type r:type a:name http://…foaf/Person a:homepage f:nom f:nom Besse, Christianne Ghosh, Amitav http://www.amitavghosh.com
33. Start making richer queries! User of dataset “F” can now query: “donnes-moi la page d’accueil de l’auteur de l’original” well… “give me the home page of the original’s ‘auteur’” The information is not in datasets “F” or “A”… …but was made available by: merging datasets “A” and datasets “F” adding three simple extra statements as an extra “glue”
34. Combine with different datasets Using, e.g., the “Person”, the dataset can be combined with other sources For example, data in Wikipedia can be extracted using dedicated tools e.g., the “dbpedia” project can extract the “infobox” information from Wikipedia already…
35. Merge with Wikipedia data a:title The Glass Palace http://…isbn/000651409X 2000 a:year Le palais des miroirs f:original f:titre a:publisher a:city London http://…isbn/2020386682 a:author Harper Collins a:p_name f:auteur f:traducteur r:type a:name r:type http://…foaf/Person a:homepage f:nom f:nom r:type Besse, Christianne Ghosh, Amitav http://www.amitavghosh.com foaf:name w:reference http://dbpedia.org/../Amitav_Ghosh
36. Merge with Wikipedia data a:title The Glass Palace http://…isbn/000651409X 2000 a:year Le palais des miroirs f:original f:titre a:publisher a:city London http://…isbn/2020386682 a:author Harper Collins a:p_name f:auteur f:traducteur r:type a:name r:type http://…foaf/Person a:homepage f:nom f:nom r:type w:isbn Besse, Christianne Ghosh, Amitav http://www.amitavghosh.com http://dbpedia.org/../The_Glass_Palace foaf:name w:reference w:author_of http://dbpedia.org/../Amitav_Ghosh w:author_of http://dbpedia.org/../The_Hungry_Tide w:author_of http://dbpedia.org/../The_Calcutta_Chromosome
37. Merge with Wikipedia data a:title The Glass Palace http://…isbn/000651409X 2000 a:year Le palais des miroirs f:original f:titre a:publisher a:city London http://…isbn/2020386682 a:author Harper Collins a:p_name f:auteur f:traducteur r:type a:name r:type http://…foaf/Person a:homepage f:nom f:nom r:type w:isbn Besse, Christianne Ghosh, Amitav http://www.amitavghosh.com http://dbpedia.org/../The_Glass_Palace foaf:name w:reference w:author_of http://dbpedia.org/../Amitav_Ghosh w:born_in http://dbpedia.org/../Kolkata w:author_of http://dbpedia.org/../The_Hungry_Tide w:lat w:long w:author_of http://dbpedia.org/../The_Calcutta_Chromosome
38. Is that surprising? It may look like it but, in fact, it should not be… What happened via automatic means is done every day by Web users! The difference: a bit of extra rigour so that machines could do this, too
39. It could become even more powerful We could add extra knowledge to the merged datasets e.g., a full classification of various types of library data geographical information etc. This is where ontologies, extra rules, etc, come in ontologies/rule sets can be relatively simple and small, or huge, or anything in between… Even more powerful queries can be asked as a result
40. What did we do? Manipulate Query … Applications Map, Expose, … Data represented in abstract format Data in various formats
41. So where is the Semantic Web? The Semantic Web provides technologies to make such integration possible! Hopefully you get a full picture at the end of the tutorial…
43. RDF triples Let us begin to formalize what we did! we “connected” the data… but a simple connection is not enough… data should be named somehow hence the RDF Triples: a labelled connection between two resources
44.
45.
46. A simple RDF example (in RDF/XML) http://…isbn/2020386682 f:original f:titre http://…isbn/000651409X Le palais des miroirs <rdf:Description rdf:about="http://…/isbn/2020386682"> <f:titre xml:lang="fr">Le palais des mirroirs</f:titre> <f:original rdf:resource="http://…/isbn/000651409X"/> </rdf:Description> (Note: namespaces are used to simplify the URI-s)
47. A simple RDF example (in Turtle) http://…isbn/2020386682 f:original f:titre http://…isbn/000651409X Le palais des miroirs <http://…/isbn/2020386682> f:titre "Le palais des mirroirs"@fr ; f:original <http://…/isbn/000651409X> .
48. A simple RDF example (in RDFa) http://…isbn/2020386682 f:original f:titre http://…isbn/000651409X Le palais des miroirs <p about="http://…/isbn/2020386682">The book entitled “<span property="f:title" lang="fr">Le palais des mirroirs</span>” is the French translation of the “<span rel="f:original" resource="http://…/isbn/000651409X">Glass Palace</span>”</p> .
49. “Internal” nodes Consider the following statement: “the publisher is a «thing» that has a name and an address” Until now, nodes were identified with a URI. But… …what is the URI of «thing»? London a:city a:publisher http://…isbn/000651409X a:p_name Harper Collins
50. One solution: create an extra URI The resource will be “visible” on the Web care should be taken to define unique URI-s <rdf:Description rdf:about="http://…/isbn/000651409X"> <a:publisher rdf:resource="urn:uuid:f60ffb40-307d-…"/> </rdf:Description> <rdf:Description rdf:about="urn:uuid:f60ffb40-307d-…"> <a:p_name>HarpersCollins</a:p_name> <a:city>HarpersCollins</a:city> </rdf:Description>
51. Internal identifier (“blank nodes”) <rdf:Description rdf:about="http://…/isbn/000651409X"> <a:publisher rdf:nodeID="A234"/> </rdf:Description> <rdf:Description rdf:nodeID="A234"> <a:p_name>HarpersCollins</a:p_name> <a:city>HarpersCollins</a:city> </rdf:Description> <http://…/isbn/2020386682> a:publisher _:A234. _:A234 a:p_name "HarpersCollins". Internal = these resources are not visible outside London a:city a:publisher http://…isbn/000651409X a:p_name Harper Collins
52. Blank nodes: the system can do it Let the system create a “nodeID” internally (you do not really care about the name…) <http://…/isbn/000651409X> a:publisher [ a:p_name "HarpersCollins"; … ]. London a:city a:publisher http://…isbn/000651409X a:p_name Harper Collins
53. Blank nodes when merging Blank nodes require attention when merging blanks nodes with identical nodeID-s in different graphs are different implementations must be careful…
54. RDF in programming practice For example, using Java+Jena (HP’s Bristol Lab): a “Model” object is created the RDF file is parsed and results stored in the Model the Model offers methods to retrieve: triples (property,object) pairs for a specific subject (subject,property) pairs for specific object etc. the rest is conventional programming… Similar tools exist in Python, PHP, etc.
55. Jena example // create a model Model model=new ModelMem(); Resource subject=model.createResource("URI_of_Subject") // 'in' refers to the input file model.read(new InputStreamReader(in)); StmtIterator iter=model.listStatements(subject,null,null); while(iter.hasNext()) { st = iter.next(); p = st.getProperty(); o = st.getObject(); do_something(p,o); }
56. Merge in practice Environments merge graphs automatically e.g., in Jena, the Model can load several files the load merges the new statements automatically merge takes care of blank node issues, too
57.
58. Keep data in databases or XML, just export key “fact” as RDF
59. Use a faceted browser to visualize and interact with the resultCourtesy of Nigel Wilkinson, Lee Harland, Pfizer Ltd, MelliyalAnnamalai, Oracle (SWEO Case Study)
61. Need for RDF schemas First step towards the “extra knowledge”: define the terms we can use what restrictions apply what extra relationships are there? Officially: “RDF Vocabulary Description Language” the term “Schema” is retained for historical reasons…
62. Classes, resources, … Think of well known traditional vocabularies: use the term “novel” “every novel is a fiction” “«The Glass Palace» is a novel” etc. RDFS defines resources and classes: everything in RDF is a “resource” “classes” are also resources, but… …they are also a collection of possible resources (i.e., “individuals”) “fiction”, “novel”, …
63. Classes, resources, … (cont.) Relationships are defined among resources: “typing”: an individual belongs to a specific class “«The Glass Palace» is a novel” to be more precise: “«http://.../000651409X» is a novel” “subclassing”: all instances of one are also the instances of the other (“every novel is a fiction”) RDFS formalizes these notions in RDF
64. Classes, resources in RDF(S) rdfs:Class rdf:type rdf:type #Novel http://…isbn/000651409X RDFS defines the meaning of these terms (these are all special URI-s, we just use the namespace abbreviation)
65. Inferred properties #Fiction rdf:type rdf:subClassOf rdf:type #Novel http://…isbn/000651409X (<http://…/isbn/000651409X> rdf:type #Fiction) is not in the original RDF data… …but can be inferred from the RDFS rules RDFS environments return that triple, too
66. Inference: let us be formal… The RDF Semantics document has a list of (33) entailment rules: “if such and such triples are in the graph, add this and this” do that recursively until the graph does not change The relevant rule for our example: If: uuu rdfs:subClassOf xxx . vvv rdf:type uuu . Then add: vvv rdf:type xxx .
67. Properties Property is a special class (rdf:Property) properties are also resources identified by URI-s There is also a possibility for a “sub-property” all resources bound by the “sub” are also bound by the other Range and domain of properties can be specified i.e., what type of resources serve as object and subject
68. Example for property characterization :title rdf:type rdf:Property; rdfs:domain :Fiction; rdfs:range rdfs:Literal.
69.
70. Literals Literals may have a data type floats, integers, booleans, etc, defined in XML Schemas full XML fragments (Natural) language can also be specified
72. A bit of RDFS can take you far… Remember the power of merge? We could have used, in our example: f:auteur is a subproperty of a:author and vice versa(although we will see other ways to do that…) Of course, in some cases, more complex knowledge is necessary (see later…)
73.
74. integrate 6 or 7 geographically distributed databases, …Michael Grove, Clark & Parsia, LLC, and Andrew Schain, NASA, (SWEO Case Study)
76. Simple approach Write RDF/XML, RDFa, or Turtle “manually” In some cases that is necessary, but it really does not scale…
77. RDF with XHTML Obviously, a huge source of information By adding some “meta” information, the same source can be reused for, eg, data integration, better mashups, etc typical example: your personal information, like address, should be readable for humans and processable by machines
78. RDF with XML/(X)HTML (cont) Two solutions have emerged: use microformats and convert the content into RDF XSLT is the favorite approach add RDF-like statements directly into XHTML via RDFa
79. Bridge to relational databases Data on the Web are mostly stored in databases “Bridges” are being defined: a layer between RDF and the relational data RDB tables are “mapped” to RDF graphs, possibly on the fly different mapping approaches are being used a number RDB systems offer this facility already (eg, Oracle, OpenLink, …) W3C is working on a standard in this area
81. Linked Open Data Project Goal: “expose” open datasets in RDF Set RDF links among the data items from different datasets Set up, if possible, query endpoints
82. Example data source: DBpedia DBpedia is a community effort to extract structured (“infobox”) information from Wikipedia provide a query endpoint to the dataset interlink the DBpedia dataset with other datasets on the Web
84. Automatic links among open datasets <http://dbpedia.org/resource/Amsterdam> owl:sameAs <http://rdf.freebase.com/ns/...> ; owl:sameAs <http://sws.geonames.org/2759793> ; ... <http://sws.geonames.org/2759793> owl:sameAs <http://dbpedia.org/resource/Amsterdam> wgs84_pos:lat "52.3666667" ; wgs84_pos:long "4.8833333"; geo:inCountry <http://www.geonames.org/countries/#NL> ; ... Processors can switch automatically from one to the other…
90. something like “give me (a,b) pairs for which there is an x such that (x parent a) and (b brother x) holds” (ie, return the uncles)
91.
92. General: graph patterns The fundamental idea: use graph patterns the pattern contains unbound symbols by binding the symbols, subgraphs of the RDF graph are selected if there is such a selection, the query returns bound resources
93. Our Jena example in SPARQL SELECT ?p ?o WHERE {subject ?p ?o} The triples in WHERE define the graph pattern, with ?p and ?o “unbound” symbols The query returns all p,o pairs ?o ?p ?o ?p subject ?o ?p ?o ?p
100. Many extra SPARQL features Limit the number of returned results; remove duplicates, sort them, … Optional branches: if some part of the pattern does not match, ignore it Specify several data sources (via URI-s) within the query (essentially, a merge on-the-fly!) Construct a graph using a separate pattern on the query results In SPARQL 1.1: updating data, not only query
101. SPARQL usage in practice SPARQL is usually used over the network separate documents define the protocol and the result format SPARQL Protocol for RDF with HTTP and SOAP bindings SPARQL results in XML or JSON formats Big datasets often offer “SPARQL endpoints” using this protocol typical example: SPARQL endpoint to DBpedia
102. SPARQL as a unifying point Application SPARQL Construct SPARQL Construct SPARQL Endpoint SPARQL Endpoint SPARQL Processor Database Triple store NLP Techniques RDFa GRDDL, RDFa SQLRDF Relational Database RDF Graph HTML Unstructured Text XML/XHTML
103.
104. around 80 databases, around 200,000 records eachCourtesy of Huajun Chen, Zhejiang University, (SWEO Case Study)
106. Vocabularies Data integration needs agreements on terms “translator”, “author” categories used “Person”, “literature” relationships among those “an author is also a Person…”, “historical fiction is a narrower term than fiction” ie, new relationships can be deduced
107. Vocabularies There is a need for “languages” to define such vocabularies to define those vocabularies to assign clear “semantics” on how new relationships can be deduced
108. But what about RDFS? Indeed RDFS is such framework: there is typing, subtyping properties can be put in a hierarchy datatypes can be defined RDFS is enough for many vocabularies But not for all!
109. Three technologies have emerged To re-use thesauri, glossaries, etc: SKOS To define more complex vocabularies with a strong logical underpinning: OWL Generic framework to define rules on terms and data: RIF
111. SKOS Represent and share classifications, glossaries, thesauri, etc for example: Dewey Decimal Classification, Art and Architecture Thesaurus, ACM classification of keywords and terms… classification/formalization of Web 2.0 type tags Define classes and properties to add those structures to an RDF universe allow for a quick port of this traditional data, combine it with other data
114. Thesauri have identical structures… The structure of the LOC page is fairly typical label, alternate label, narrower, broader, … there is even an ISO standard for such structures SKOS provides a basic structure to create an RDF representation of these
116. Usage of the LOC graph Fiction skos:Concept Historical Fiction skos:prefLabel skos:prefLabel rdf:type skos:broader dc:subject dc:title http:.//…/isbn/… The GlassPalace
117. Importance of SKOS SKOS provides a simple bridge between the “print world” and the (Semantic) Web Thesauri, glossaries, etc, from the library community can be made available LOC is a good example SKOS can also be used to organize tags, annotate other vocabularies, …
118. Importance of SKOS Anybody in the World can refer to common concepts they mean the same for everybody Applications may exploit the relationships among concepts eg, SPARQL queries may be issued on the merge of the library data and the LOC terms
119. Semantic portal for art collections Courtesy of Jacco van Ossenbruggen, CWI, and Guus Schreiber, VU Amsterdam
121. SKOS is not enough… SKOS may be used to provide simple vocabularies But it is not a complete solution it concentrates on the concepts only no characterization of properties in general simple from a logical perspective ie, few inferences are possible
122. Application may want more… Complex applications may want more possibilities: characterization of properties identification of objects with different URI-s disjointness or equivalence of classes construct classes, not only name them more complex classification schemes can a program reason about some terms? E.g.: “if «Person» resources «A» and «B» have the same «foaf:email» property, then «A» and «B» are identical” etc.
123. Web Ontology Language = OWL OWL is an extra layer, a bit like RDFS or SKOS own namespace, own terms it relies on RDF Schemas It is a separate recommendation actually… there is a 2004 version of OWL (“OWL 1”) and there is an update (“OWL 2”) published in 2009
124. OWL is complex… OWL is a large set of additional terms We will not cover the whole thing here…
125. Term equivalences For classes: owl:equivalentClass: two classes have the same individuals owl:disjointWith: no individuals in common For properties: owl:equivalentProperty remember the a:author vs. f:auteur? owl:propertyDisjointWith
126. Term equivalences For individuals: owl:sameAs: two URIs refer to the same concept (“individual”) owl:differentFrom: negation of owl:sameAs
127. Other example: connecting to French owl:equivalentClass a:Novel f:Roman owl:equivalentProperty a:author f:auteur
128.
129. What this means is… If the following holds in our triples: :email rdf:type owl:InverseFunctionalProperty.
130. What this means is… If the following holds in our triples: :email rdf:type owl:InverseFunctionalProperty. <A> :email "mailto:a@b.c". <B> :email "mailto:a@b.c".
131. What this means is… If the following holds in our triples: :email rdf:type owl:InverseFunctionalProperty. <A> :email "mailto:a@b.c". <B> :email "mailto:a@b.c". then, processed through OWL, the following holds, too: <A> owl:sameAs <B>.
132. Keys Inverse functional properties are important for identification of individuals think of the email examples But… identification based on one property may not be enough
133. Keys “if two persons have the same emails and the same homepages then they are identical” Identification is based on the identical values of two properties The rule applies to persons only
135. What it means is… If: <A> rdf:type :Person ; :email "mailto:a@b.c"; :homepage "http://www.ex.org". <B> rdf:type :Person ; :email "mailto:a@b.c"; :homepage "http://www.ex.org". then, processed through OWL, the following holds, too: <A> owl:sameAs <B>.
136. Classes in OWL In RDFS, you can subclass existing classes… that’s all In OWL, you can construct classes from existing ones: enumerate its content through intersection, union, complement etc
137. Enumerate class content :Currency rdf:type owl:Class; owl:oneOf (:€ :£ :$). I.e., the class consists of exactly of those individuals and nothing else
138. Union of classes :Novel rdf:type owl:Class. :Short_Story rdf:type owl:Class. :Poetry rdf:type owl:Class. :Literature rdf:type owl:Class; owl:unionOf (:Novel :Short_Story :Poetry). Other possibilities: owl:complementOf, owl:intersectionOf, …
139. For example… If: :Novel rdf:type owl:Class. :Short_Story rdf:type owl:Class. :Poetry rdf:type owl:Class. :Literature rdf:type owl:Class; owl:unionOf (:Novel :Short_Story :Poetry). <myWork> rdf:type :Novel . then the following holds, too: <myWork> rdf:type :Literature .
140. It can be a bit more complicated… If: :Novel rdf:type owl:Class. :Short_Story rdf:type owl:Class. :Poetry rdf:type owl:Class. :Literature rdf:type owlClass; owl:unionOf (:Novel :Short_Story :Poetry). fr:Roman owl:equivalentClass :Novel . <myWork> rdf:type fr:Roman . then, through the combination of different terms, the following still holds: <myWork> rdf:type :Literature .
141. What we have so far… The OWL features listed so far are already fairly powerful E.g., various databases can be linked via owl:sameAs, functional or inverse functional properties, etc. Many inferred relationship can be found using a traditional rule engine
142. However… that may not be enough Very large vocabularies might require even more complex features some major issues the way classes (i.e., “concepts”) are defined handling of datatypes like intervals OWL includes those extra features but… the inference engines become (much) more complex
143. Example: property value restrictions New classes are created by restricting the property values on a class For example: how would I characterize a “listed price”? it is a price that is given in one of the “allowed” currencies (€, £, or $) this defines a new class
144. But: OWL is hard! The combination of class constructions with various restrictions is extremely powerful What we have so far follows the same logic as before extend the basic RDF and RDFS possibilities with new features define their semantics, ie, what they “mean” in terms of relationships expect to infer new relationships based on those However… a full inference procedure is hard not implementable with simple rule engines, for example
145. OWL “species” or profiles OWL species comes to the fore: restricting which terms can be used and under what circumstances (restrictions) if one abides to those restrictions, then simpler inference engines can be used They reflect compromises: expressiveness vs. implementability
147. OWL RL Goal: to be implementable with rule engines Usage follows a similar approach to RDFS: merge the ontology and the instance data into an RDF graph use the rule engine to add new triples (as long as it is possible)
148. What can be done in OWL RL? Many features are available: identity of classes, instances, properties subproperties, subclasses, domains, ranges union and intersection of classes (but with some restrictions) property characterizations (functional, symmetric, etc) property chains keys some property restrictions All examples so far could be inferred with OWL RL!
149. Improved Search via Ontology (GoPubMed) Search results are re-ranked using ontologies related terms are highlighted
150.
151.
152. Why rules on the Semantic Web? Some conditions may be complicated in ontologies (ie, OWL) eg, Horn rules: (P1 & P2 & …) -> C In many cases applications just want 2-3 rules to complete integration Ie, rules may be an alternative to (OWL based) ontologies
153. Things you may want to express An example from our bookshop integration: “I buy a novel with over 500 pages if it costs less than €20” something like (in an ad-hoc syntax): { ?x rdf:type p:Novel; p:page_number ?n; p:price [ p:currency :€; rdf:value ?z ]. ?n > "500"^^xsd:integer. ?z < "20.0"^^xsd:double. } => { <me> p:buys ?x }
154. Things you may want to express p:Novel rdf:type ?n p:page_number ?n>500 p:buys ?x ?x me p:price :€ p:currency rdf:value ?z ?z<20
155. RIF (Rule Interchange Format) The goals of the RIF work: define simple rule language(s) for the (Semantic) Web define interchange formats for rule based systems RIF defines several “dialects” of languages RIF is not bound to RDF only eg, relationships may involve more than 2 entities there are dialects for production rule systems
156. RIF Core The simplest RIF “dialect” A Core document is directives like import, prefix settings for URI-s, etc a sequence of logical implications
157. RIF Core example Document( Prefix(cpt http://example.com/concepts#) Prefix(person http://example.com/people#) Prefix(isbn http://…/isbn/) Group ( Forall ?Buyer ?Book ?Seller ( cpt:buy(?Buyer ?Book ?Seller):- cpt:sell(?Seller ?Book ?Buyer) ) cpt:sell(person:John isbn:000651409X person:Mary) ) ) This infers the following relationship: cpt:buy(person:Mary isbn:000651409X person:John)
158. Expressivity of RIF Core Formally: definite Horn without function symbols, a.k.a. “Datalog” eg, p(a,b,c) is fine, but p(f(a),b,c) is not Includes some extra features built-in datatypes and predicates “local” symbols, a bit like blank nodes
159. Expressivity of RIF Core There are also “safeness measures” eg, variable in a consequent should be in the antecedent this secures a straightforward implementation strategy (“forward chaining”)
160. RIF Syntaxes RIF defines a “presentation syntax” a standard XML syntax to encode and exchange the rules there is a draft for expressing Core in RDF just like OWL is represented in RDF
161. What about RDF and RIF? Typical scenario: the “data” of the application is available in RDF rules on that data is described using RIF the two sets are “bound” (eg, RIF “imports” the data) a RIF processor produces new relationships
162. To make RIF/RDF work Some technical issues should be settled: RDF triples have to be representable in RIF various constructions (typing, datatypes, lists) should be aligned the semantics of the two worlds should be compatible There is a separate document that brings these together
163. Remember the what we wanted from Rules? { ?x rdf:type p:Novel; p:page_number ?n; p:price [ p:currency :€; rdf:value ?z ]. ?n > "500"^^xsd:integer. ?z < "20.0"^^xsd:double. } => { <me> p:buys ?x }
164. The same with RIF Presentation syntax Document ( Prefix … Group ( Forall ?x ?n ?z ( <me>[p:buys->?x] :- And( ?x rdf:type p:Novel ?x[p:page_number->?n p:price->_abc] _abc[p:currency->:€ rdf:value->?z] External( pred:numeric-greater-than(?n "500"^^xsd:integer) ) External( pred:numeric-less-than(?z "20.0"^^xsd:double) ) ) ) ) )
168. RIF vs. OWL? The expressivity of the two is fairly identical the emphasis are a bit different Using rules vs. ontologies may largely depend on available tools personal technical experience and expertise taste…
169. What about OWL RL? OWL RL stands for “Rule Language”… OWL RL is in the intersection of RIF Core and OWL inferences in OWL RL can be expressed with RIF rules RIF Core engines can act as OWL RL engines
170. Inferencing and SPARQL Question: how do SPARQL queries and inferences work together? RDFS, OWL, and RIF produce new relationships on what data do we query? Answer: in current SPARQL, that is not defined But, in SPARQL 1.1 it is…
171. SPARQL 1.1 and RDFS/OWL/RIF SPARQL Engine with entailment RDF Data Query result entailment RDFS/OWL/RIF data SPARQL Pattern SPARQL Pattern RDF Data with extra triples pattern matching
172. What have we achieved?(putting all this together)
173. Remember the integration example? Manipulate Query … Applications Map, Expose, … Data represented in abstract format Data in various formats
174. Same with what we learned SPARQL, Inferences … Applications RDB RDF, GRDL, RDFa, … Data represented in RDF with extra knowledge (RDFS, SKOS, RIF, OWL,…) Data in various formats
175.
176. Use rules on the RDF data to provide a proper itinerary Courtesy of Jesús Fernández, Mun. of Zaragoza, and Antonio Campos, CTIC (SWEO Use Case)
178. Available specifications: Primers, Guides The “RDF Primer” and the “OWL Guide” give a formal introduction to RDF(S) and OWL SKOS has its separate “SKOS Primer” GRDDL Primer and RDFa Primer have been published The W3C Semantic Web Activity Wiki has links to all the specifications
179. “Core” vocabularies There are also a number “core vocabularies” Dublin Core: about information resources, digital libraries, with extensions for rights, permissions, digital right management FOAF: about people and their organizations DOAP: on the descriptions of software projects SIOC: Semantically-Interlinked Online Communities vCard in RDF … One should never forget: ontologies/vocabularies must be shared and reused!
180. Some books J. Pollock: Semantic Web for Dummies, 2009 G. Antoniu and F. van Harmelen: Semantic Web Primer, 2nd edition in 2008 D. Allemang and J. Hendler: Semantic Web for the Working Ontologist, 2008 P. Hitzler, R. Sebastian, M. Krötzsch: Foundation of Semantic Web Technologies, 2009 … See the separate Wiki page collecting book references
199. Thank you for your attention! These slides are also available on the Web: http://www.w3.org/2010/Talks/0622-SemTech-IH/
Editor's Notes
The point is: they combine data drawn from data.gov.uk to produce, traditional, printed paper to be distributed in the neighborhood providing practical information like doctors, pharmacies, etc, in an up-to-date fashion
List of universities come from Dbpedia -> auto completion based on that list -> NYT identifiers are present in DBPedia, used to back index into the NYT archives; sameAs links to, say, freeBase is also provided